Papers with CEO
What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues (P19-1)
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| Challenge: | Existing studies have shown that textual information in a firm’s financial statement can be used to predict its stock’s risk level. |
| Approach: | They propose to model CEO’s verbal (from text) and vocal (from audio) information in a conference call. |
| Outcome: | The proposed model reduces the error rate by comparing CEO’s verbal and vocal information in a conference call with other models. |
CEO: Corpus-based Open-Domain Event Ontology Induction (2024.findings-eacl)
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| Challenge: | Existing event-centric NLP models restrict their generalization capabilities by limiting the pre-defined ontology. |
| Approach: | They propose a Corpus-based Event Ontology induction model to relax the restriction imposed by pre-defined ontologies. |
| Outcome: | The proposed model can induce a hierarchical event ontology with meaningful names on eleven open-domain corpora, making it more trustworthy and easier to be further curated. |
Leveraging 3D Gaussian for Temporal Knowledge Graph Embedding (2025.findings-emnlp)
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| Challenge: | Representation learning in knowledge graphs (KGs) has focused on static data, yet many real-world knowledge graph are inherently dynamic. |
| Approach: | They propose a temporal embedding method inspired by 3D Gaussian Splatting where entities, relations, and timestamps are modeled as 3D gaussian distributions with learnable structured covariance. |
| Outcome: | The proposed method outperforms state-of-the-art methods on three benchmark TKG datasets. |
BERT for Coreference Resolution: Baselines and Analysis (D19-1)
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| Challenge: | Recent BERT-based models have reported dramatic gains on multiple semantic benchmarks including question-answering, natural language inference, and named entity recognition. |
| Approach: | They apply BERT to coreference resolution, achieving a new state of the art on the GAP and OntoNotes benchmarks. |
| Outcome: | A qualitative analysis of model predictions shows that BERT-large is better at distinguishing between related but distinct entities, but there is room for improvement in modeling document-level context, conversations, and mention paraphrasing. |
Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities (2022.emnlp-main)
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| Challenge: | Existing topic-based novelty detection methods do not perform semantic reasoning involving relations between named entities in text and their background knowledge. |
| Approach: | They propose a model to detect whether a text is novel or not . they propose to use a factual text to characterize novelty. |
| Outcome: | The proposed model outperforms 10 baselines by large margins on the novelty detection task. |
The Circumstantial Event Ontology (CEO) and ECB+/CEO: an Ontology and Corpus for Implicit Causal Relations between Events (L18-1)
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| Challenge: | a new ontology for calamity events models semantic circumstantial relations between event classes . a circumstancial relation makes clear "why" something happened, without necessarily predicting it. |
| Approach: | They propose a circumstantial event ontology that models semantic circumstancial relations between event classes . they propose ECB+ annotated corpus for circumstantal relations and a meta model . |
| Outcome: | The proposed model captures that the change yielded by one event explains to people the happening of the next event when observed. |